SOTAVerified

Image Classification

Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.

Source: Metamorphic Testing for Object Detection Systems

Papers

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Showing 2401–2450 of 10419 papers

TitleStatusHype
DLBricks: Composable Benchmark Generation to Reduce Deep Learning Benchmarking Effort on CPUs (Extended)—0
Combining Spiking Neural Network and Artificial Neural Network for Enhanced Image Classification—0
Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification—0
Are Sample-Efficient NLP Models More Robust?—0
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers—0
Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers—0
Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?—0
A Bandit Approach with Evolutionary Operators for Model Selection—0
Combining pretrained CNN feature extractors to enhance clustering of complex natural images—0
Combining multiscale features for classification of hyperspectral images: a sequence based kernel approach—0
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?—0
Combining multiple resolutions into hierarchical representations for kernel-based image classification—0
Adversarial YOLO: Defense Human Detection Patch Attacks via Detecting Adversarial Patches—0
Do Convolutional Neural Networks Learn Class Hierarchy?—0
Adversarial Transformations for Semi-Supervised Learning—0
Creating Ensembles of Classifiers through UMDA for Aerial Scene Classification—0
Combining Deep Learning with Good Old-Fashioned Machine Learning—0
A Relational Model for One-Shot Classification—0
A Contrastive Learning Approach to Auroral Identification and Classification—0
Combinets: Creativity via Recombination of Neural Networks—0
Combined Use of Federated Learning and Image Encryption for Privacy-Preserving Image Classification with Vision Transformer—0
Are Gradient-based Saliency Maps Useful in Deep Reinforcement Learning?—0
Combined statistical and model based texture features for improved image classification—0
Combined Scaling for Zero-shot Transfer Learning—0
Are Deep Learning Models Robust to Partial Object Occlusion in Visual Recognition Tasks?—0
Adversarial Transfer of Pose Estimation Regression—0
Advancements in Image Classification using Convolutional Neural Network—0
COMBINED FLEXIBLE ACTIVATION FUNCTIONS FOR DEEP NEURAL NETWORKS—0
Are Classification Robustness and Explanation Robustness Really Strongly Correlated? An Analysis Through Input Loss Landscape—0
Adversarial Training for Relation Extraction—0
Combined convolutional and recurrent neural networks for hierarchical classification of images—0
Differentiable Combinatorial Losses through Generalized Gradients of Linear Programs—0
Are All Vision Models Created Equal? A Study of the Open-Loop to Closed-Loop Causality Gap—0
A Bag of Visual Words Model for Medical Image Retrieval—0
Combating Noisy Labels in Long-Tailed Image Classification—0
Are all negatives created equal in contrastive instance discrimination?—0
Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification—0
Adversarial Training for Graph Neural Networks via Graph Subspace Energy Optimization—0
Diversity Matters When Learning From Ensembles—0
Color-S^4L: Self-supervised Semi-supervised Learning with Image Colorization—0
Color-NeuraCrypt: Privacy-Preserving Color-Image Classification Using Extended Random Neural Networks—0
ArchNet: Data Hiding Model in Distributed Machine Learning System—0
Diversified Ensembling: An Experiment in Crowdsourced Machine Learning—0
Color Image Classification via Quaternion Principal Component Analysis Network—0
Diverse Feature Learning by Self-distillation and Reset—0
Color and Shape Content Based Image Classification using RBF Network and PSO Technique: A Survey—0
Divergent Search for Few-Shot Image Classification—0
Diversifying Sample Generation for Accurate Data-Free Quantization—0
COLORA: Efficient Fine-Tuning for Convolutional Models with a Study Case on Optical Coherence Tomography Image Classification—0
Co-localization with Category-Consistent Features and Geodesic Distance Propagation—0
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All datasetsImageNetCIFAR-10CIFAR-100STL-10ObjectNetMNISTSVHNiNaturalist 2018ImageNet ReaLFlowers-102Clothing1Mmini WebVision 1.0

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CoCa (finetuned)Top 1 Accuracy91—Unverified
2Model soups (BASIC-L)Top 1 Accuracy90.98—Unverified
3Model soups (ViT-G/14)Top 1 Accuracy90.94—Unverified
4DaViT-GTop 1 Accuracy90.4—Unverified
5Meta Pseudo Labels (EfficientNet-L2)Top 1 Accuracy90.2—Unverified
6DaViT-HTop 1 Accuracy90.2—Unverified
7SwinV2-GTop 1 Accuracy90.17—Unverified
8MAWS (ViT-6.5B)Top 1 Accuracy90.1—Unverified
9Florence-CoSwin-HTop 1 Accuracy90.05—Unverified
10RevCol-HTop 1 Accuracy90—Unverified